Exercise training system and method

ABSTRACT

A method of analysing a resistance exercise activity executed by a subject is disclosed. In an embodiment, the method includes: receiving a plurality of exercise parameter values for the executed exercise activity into a processing device, the plurality of exercise parameter values including information representing an execution profile (EP) for the executed exercise activity; accessing a store of information to retrieve information representing a load profile (LP) for the executed exercise activity; and processing the plurality of exercise parameter values and the information representing the load profile to determine one or more assessment parameter values for assessing the executed exercise activity.

FIELD OF THE INVENTION

The present invention relates to exercise training for skeletal musclegrowth. In a typical application an embodiment of the present inventionmay find application in an exercise training programme, such as aresistance training program.

BACKGROUND OF THE INVENTION

Resistance training is the practice of placing a subject's skeletalmuscles under load, typically via eccentric and concentric contractionsof a prescribed movement for a number of consecutive repetitions(“reps”) which may be repeated several times (“sets”) with a restbetween each set. In some forms of resistance training, the aim of thetraining is to elicit an increase in muscle size or strength or both.

Generally speaking, conventional training methods employ variations ofthe set/repetition schemes and/or variations of contraction profiles(exercise execution) to stimulate (stress) a muscle or muscles andgenerate a response in the muscle or muscles worked by the exercise,with some methods being more effective than others and different foreach individual due to genetic/physiological differences. For example, aset/rep type exercise may be designed to create a training responsewhich is biased towards a particular outcome, such as heavy weightswith, for example, 4 reps or less for predominantly strength gains and,for example, 8 to 12 reps for size gains (hypertrophy) of a muscle groupworked by the exercise.

Furthermore, within these conventional training methods there are othervariables which may be varied for the performance of the ‘exercises’,including but not limited to:

-   -   the speed of the repetition, both in a concentric phase and an        eccentric phase of a muscle activation and the ‘pause’ between        both;    -   the ‘tempo’ of the repetition, being the combination of all        phases;    -   the load profile, which may be considered as the “effective”        load on the working muscles at each stage of the exercise as the        muscles contract and the forces vary due to, for example, a        changing angle of incidence of gravity in relation to the        skeletal structure, changing factors of leverage of the joint(s)        and tendon(s) involved, or the load profile of, for example, a        camshaft if an exercise machine incorporating a cable and/or        camshaft is used, or simply the technique of the exercise        performance such that the trainee deliberately alters their        stance, position, or limbs during the exercise to elicit a        particular load effect; and    -   various ‘overload’ techniques designed to increase the overall        stress on the muscle—including but not limited to: forced reps,        rest-pause, pre-exhaust exercises, drop sets, compound sets,        giant sets, and “x” reps.

A fundamental consideration when preparing a training programmeincorporating exercise activities is that every person is different.Also, for each person, the various muscles will be different in theirability to handle and recover from stresses. In this respect, musclesare recognised as including various fibre “types”, broadly classified as“fast-twitch” and “slow-twitch” fibres, and also as having have severalsub-categories. Each fibre “type” is recognised as having abilitiesbiased towards particular training loads. For example, slow-twitchfibres are recognised as being better in endurance events, whereasfast-twitch fibres are recognised as being better at short termexplosive or power events.

It is also known that the different fibre types will come into play atvarious stages of physical performance based upon the loads and durationof an exercise or physical activity.

Due to physiological differences between trainees, such as thoseoutlined above, the variable options of training schemes, the time ittakes to notice results, and various other factors which may impact onthe training response of a trainee during training (such as, nutritionalvariations, sleep patterns, psychological stresses and the like), thereis often significant confusion as to how to achieve a desired trainingeffect, and thus which type of training approach to adopt. Consequently,trainees may employ a protocol (such as a training program) that is notoptimised for their physiology, or at least not tailored for, theirdesired objectives, or other circumstances. As an example, a trainee mayadopt a training program from a sports person they admire, unaware of,or ignoring the fact that, that sports person has different geneticstructure, or lives a different lifestyle, amongst other things.

Another difficulty with existing “generic” training protocols is thatthey may have prescribed set, rep, and recovery schemes which ignore thephysiological differences between people. Because of this, any “fixed”training “scheme” may only be suitable for a small percentage of thepopulation, and indeed only suited for a period of time until thetrainee adapts to that scheme, or reaches a different “stage” in theirlife either due to their training progression, age, health, or livingconditions.

SUMMARY OF THE INVENTION

A first aspect of the present invention provides a method of analysing aresistance exercise activity executed by a subject, the methodincluding:

-   -   a) receiving a plurality of exercise parameter values for the        executed exercise activity into a processing device, the        plurality of exercise parameter values including information        representing an execution profile (EP) for the executed exercise        activity;    -   b) accessing a store of information to retrieve information        representing a load profile (LP) for the executed exercise        activity; and    -   c) processing the plurality of exercise parameter values and the        information representing the load profile to determine one or        more assessment parameter values for assessing the executed        exercise activity.

Examples of resistance training exercise include strength trainingexercises such as isotonic and/or isometric exercises. The exerciseactivity may involve exercise equipment such as resistance bands, freeweights, or exercise machines. Examples of isotonic exercises includesquats, bench press, lat pull downs, dumbbell flyes, cable flyes, barbell curls, calf raises, chin ups, sit ups, push-ups, and the like.

The exercise activity may involve one or more weights (wt), one or moresets (s) including one or more repetitions (R), and a total activity(elapsed) time (T_(e)) for the exercise. In an embodiment, the pluralityof exercise parameters include:

-   -   a) a set parameter value (n) representing the number of the one        or more sets for the resistance training activity;    -   b) a weight parameter value (wt_(s)) for the weight associated        with each repetition within a set (s);

c) a repetition parameter value (R_(s)) representing the number of oneor more repetitions in a s where s=1 to n; and

-   -   d) a total activity (elapsed) time parameter value (T_(e)).

In one embodiment, the execution profile information and the loadprofile information include corresponding sequences of values, whereineach value represents a duration of a different exercise phase of theexercise activity. The values representing the duration of differentexercise phases of the exercise activity may include values representingthe duration of:

-   -   a) an eccentric phase;    -   b) an eccentric-pause phase;

c) a concentric phase; and

-   -   d) a concentric-pause phase.

The load profile (LP) information may be expressed as:

LP=[d ₁ ,d ₂ ,d ₃ ,d ₄]

where:

-   -   d₁=value indicating whether the eccentric phase is intended to        contribute to stress;    -   d₂=value indicating whether the eccentric-pause phase is        intended to contribute to stress;    -   d₃=value indicating whether the concentric phase is intended to        contribute to stress; and    -   d₄=value indicating whether the concentric-pause phase is        intended to contribute to stress.

In an embodiment, the execution profile (EP) information is expressedas:

EP=[t ₁ ,t ₂ ,t ₃ ,t ₄]

where:

-   -   t₁=the duration of the eccentric phase;    -   t₂=the duration of the eccentric-pause phase;    -   t₃=the duration of the concentric phase; and    -   t₄=the duration of the concentric-pause phase.

Processing the plurality of exercise parameters values and the activityinformation to determine one or more assessment parameter values forassessing the executed exercise activity may include multiplyingcorresponding values of the actual execution information and the loadprofile information to obtain a sequence of products, and summing theproducts to obtain a single value indicative of a working time undertension (TUT) for a muscle targeted or intended to be activated by theexercise activity. For example, wherein for each repetition (r) within aset (s), the single value indicative of a working time under tension(TUT) may be determined as:

TUT _(r) =t ₁ ·d ₁ +t ₂ ·d ₂ +t ₃ ·d ₃ +t ₄ ·d ₄

or more generally expressed for a set (s) as the average total timeunder tension (TUT_(R)) per repetition within the set (s) including anumber of repetitions (R_(s)) as:

${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}\; {t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$

in cases where each repetition in a set (s) involves the same loadprofile and execution profile, and each set includes a number (R_(s)) ofrepetitions.

It is preferred that the one or more assessment parameter values forassessing the executed exercise activity include:

-   a) a work volume parameter value for the executed exercise activity    (W);-   b) a work intensity parameter value for the executed exercise    activity (W_(i));-   c) a stress intensity parameter value for the executed exercise    activity (S_(i)); and-   d) a hypertrophy factor parameter value for the executed exercise    activity (H_(f)).

In an embodiment, the work volume parameter value (W) may be determinedas:

W=Σ _(s=1) ^(n) =W _(s)=Σ_(s=1) ^(n)(R _(s) ·wt _(s))

where:

-   -   n is the number of sets in the exercise activity;    -   R_(s) is the number of reps in set s, where s=1 to n; and    -   wt_(s) is the weight associated with the set where s=1 to n.

The work intensity parameter value (W_(t)) may be determined as:

$W_{i} = \frac{W}{T_{e}}$

Where Te is the elapsed time of the exercise activity.

The stress intensity parameter value (S_(i)) may be determined as:

$S_{i} = \frac{\sum\limits_{s = 1}^{n}\; {R_{s} \cdot {wt}_{s} \cdot {TUT}_{R}}}{T_{e}}$

The hypertrophy factor parameter value (H_(f)) may be determined as:

H _(f) =S _(i) ·W

Some embodiments of the present invention thus relate to a system andmethod which assesses a plurality of exercise parameters relating toresistance training, including a “hypertrophy factor” (H_(f)) that isproportional to stress intensity and work capacity of a muscle group(s),to guide the subject to set a tailored target for improved trainingresults.

A second aspect of the present invention provides a computer readablemedia including computer program instructions which are executable by aprocessor to implement a method according to the first aspect of thepresent invention.

In a third aspect of the present invention there is provided a systemfor analysing a resistance exercise activity executed by a subject, thesystem including:

-   a) a processing unit programmed with a set of program instructions    in the form of a computer software program;-   b) a store of information representing a load profile (LP) for the    executed exercise activity; and-   c) means for receiving a plurality of exercise parameter values for    the executed exercise activity into a processing device, the    plurality of exercise parameter values including information    representing an execution profile (EP) for the executed exercise    activity;    wherein the processing unit retrieves the information representing    the load profile (LP) for the executed exercise activity, and    processes the plurality of exercise parameter values and the    information representing the load profile to determine one or more    assessment parameter values for assessing the executed exercise    activity.

In a fourth aspect of the present invention there is provided a methodof determining plural sets of instructions including one or moreexercise parameters for execution of a resistance exercise activity by asubject, the method including:

-   -   accessing a store of information to retrieve a set of parameter        values attributable to the subject's prior performance of the        exercise activity;    -   processing a user-selected adjustment of a target criteria for        the exercise activity and the retrieved set of parameter values        to determine the at least one set of instructions including the        one or more exercise parameters for executing an exercise        activity to be selected by the user; and    -   outputting the at least one set of instructions for the selected        exercise activity.

According to a fifth aspect of the present invention there is provided asystem for determining one or more exercise parameters for execution ofa resistance exercise activity by a subject, the system including:

-   a) a processing unit programmed with a set of program instructions    in the form of a computer software program;-   b) a store of information in the form of a set of parameter values    attributable to the subject's prior performance of the exercise    activity; and-   c) wherein the computer software program is executable by the    processor to cause the processor to:    -   obtain the subject's target criteria for one or more exercises        objectives;    -   retrieve from the store of information a set of parameter values        attributable to the subject's prior performance of the exercise        activity;    -   process the target criteria and the set of parameter values to        determine at least one set of instructions including the one or        more exercise parameters for executing the exercise activity;        and    -   output the at least one set of instructions.

Yet another aspect of the present invention provides a method ofanalysing a resistance exercise activity executed by a subject, themethod including:

-   -   receiving a plurality of exercise parameter values for the        executed exercise activity into a processing device, the        plurality of exercise parameter values including information        representing an execution profile (EP) for the executed exercise        activity;    -   accessing a store of information to retrieve information        representing a load profile (LP) for the executed exercise        activity; and    -   processing the plurality of exercise parameter values and the        information representing the load profile to determine one or        more assessment parameter values for assessing the executed        exercise activity;    -   wherein the execution profile information and the load profile        information include corresponding sequences of values associated        with a different exercise phase of the exercise activity for a        muscle of the subject intended to perform work during the        exercise activity, such that the values associated with        different exercise phases of the exercise activity include        values associated with:    -   an eccentric phase;    -   an eccentric-pause phase;    -   an concentric phase; and    -   a concentric-pause phase;    -   and wherein the load profile (LP) information identifies the        exercise phases intended to contribute to work during execution        of the exercise activity, and the execution profile (EP)        information identifies the duration of each exercise phase which        contributed to work during execution of the exercise activity

Embodiments of the present invention may provide a method of analysisand/or predictive modeling of resistance training parameters whichprovides a tool which may be used to customise exercise activities foran individual based on their individual physiological characteristicsand training response as determined from past performance. For example,embodiments of the present invention may provide analysis of, andpredictive modelling for, a subject (such as an individual trainee) tofirstly determine training parameters for achieving their training goalsvia iterative analysis and guidance. It is further possible thatembodiments may provide feedback to adjust a program of exerciseactivity to maintain progress towards the subject's training objectivesirrespective of potential changes in the trainee's physiology or othervariables that can affect the training response.

Embodiments of the present invention may thus assist in overcomingobstacles with regard to ‘knowing’ a plurality of factors thatcontribute to athletic performance for example, a trainee's musclemake-up (via biopsies) and recuperative abilities, energy systems,oxygen delivery, hormonal responses, (i.e. ‘the system’ characteristics)and the subsequent assumptions of the ‘optimal’ training that maytherefore result, by assessing the actual performance of the total‘system’ and providing feedback and guidance to ‘peak’ trainingparameters via predictive modelling.

Embodiments of the present invention may provide a means of analysingperformance of an exercise activity via exercise parameters which may berecorded during execution of an exercise activity without requiringcomplex technical equipment or measurement instruments. Embodiments ofthe present invention may thus be simple to implement in a trainingenvironment without requiring any modifications to the existing trainingequipment, such as gym equipment.

Furthermore, by monitoring training outputs (performance statisticsoutlined earlier) against training input (for example, weight,repetitions, number of sets, load profile, time parameters outlinedearlier) embodiments of the present invention may tailor or at leastcustomise to some extent the training stimulus for a trainee.

This approach adopted by the present invention is expected to provideimproved accuracy compared to technical equipment or measurementinstruments which focus on one or more factors of performance (forexample, muscle fibre type) and which do not take into account otherfactors of performance (for example, energy systems or oxygen delivery)that may ultimately affect the actual stress that can be imposed on themuscle and thus may result on a lower stress to the muscle thandetermined by the assumptions. This problem is particularly evident withforms of resistance training which work on the principle of knowing orassuming one or more parameters and which then make assumptions inrelation to other parameters based on generic paradigms of a person'sphysiology.

The present invention may involve determining the optimal peak stressand workload and “hypertrophy factor” by monitoring and analysingexercise parameters and analysing parameters to provide performancestatistics as well as predictive training parameters and targets toaccelerate progress towards the subject's training goals.

Embodiments of the present invention may relate to an automated systemfor resistance training which thus provides tailored training protocolsfor each individual trainee to guide them to their optimal trainingparameters for each muscle and exercise. It is preferred that the systemuses analysis of training parameters that may be measured withoutspecialised equipment and uses analysis and predictive modeling toiteratively determine the optimal training mode for each exercise and/ormuscle for each trainee.

Some embodiments of the present invention may address shortcomings withtraining protocols which rely on prescribed set, repetition, andrecovery schemes and ignore the physiological differences betweenpeople. Such training protocols may only be suitable for a smallpercentage of people whose physiology suits those protocols and, eventhen, for a period of time until a trainee adapts to that protocol orperhaps reaches a different “stage” in their life either due to theirage, health, living conditions, or other circumstances.

An advantage of the present invention is that it may provide anassessment and iterative process which is capable of tailoring exerciseactivities for the subject.

BRIEF DESCRIPTION OF THE DRAWINGS

An illustrative embodiment of the present invention will be discussedwith reference to the accompanying drawings wherein:

FIG. 1 is a block diagram of a system according to an embodiment of thepresent invention;

FIG. 2 is a flow diagram for a method according to an embodiment of thepresent invention;

FIG. 3 is a table including load profile and execution profileinformation for an exercise activity;

FIG. 4 is a table including load profile and execution profileinformation for a plurality of exercise activities;

FIG. 5 is a diagram illustrating example bio-mechanical processes forexecuting a bicep curl exercise activity to illustrate the effects onexercise performance on the load profile;

FIG. 6 is a table including load profile and weight information for aplurality of exercise activities;

FIG. 7 is a table listing weight and repetition information withreference to a one-repetition maximum value for an exercise activity;and

FIG. 8 is an example output report including plural instructions forexecuting an exercise activity.

DESCRIPTION OF PREFERRED EMBODIMENT

A detailed description of one or more preferred embodiments of theinvention is provided below along with accompanying figures thatillustrate by way of example the principles of the invention. While theinvention is described in connection with such embodiments, it should beunderstood that the invention is not limited to any embodiment. On thecontrary, the scope of the invention is limited only by the appendedclaims and the invention encompasses numerous alternatives,modifications, and equivalents. For the purpose of example, numerousspecific details are set forth in the following description in order toprovide a thorough understanding of the present invention. The presentinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the present invention is notunnecessarily obscured.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.For a hardware implementation, processing may be implemented within oneor more application specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof. Software modules, also known ascomputer programs, computer codes, or instructions, may contain a numberof source code or object code segments or instructions, and may residein any computer readable medium such as a RAM memory, flash memory, ROMmemory, EPROM memory, registers, hard disk, a removable disk, a CD-ROM,a DVD-ROM or any other form of computer readable medium. In thealternative, the computer readable medium may be integral to theprocessor. The processor and the computer readable medium may reside inan ASIC or related device. The software codes may be stored in a memoryunit and executed by a processor. The memory unit may be implementedwithin the processor or external to the processor, in which case it canbe communicatively coupled to the processor via various means as isknown in the art.

The term “software,” as used here in, includes but is not limited to oneor more computer readable and/or executable instructions that cause acomputer or other electronic device to perform functions, actions,and/or behave in a desired manner. The instructions may be embodied invarious forms such as routines, algorithms, modules or programsincluding separate applications or code from dynamically linkedlibraries. Software may also be implemented in various forms such as astand-alone program, a function call, a servlet, an applet, instructionsstored in a memory, part of an operating system or other type ofexecutable instructions. It will be appreciated by one of ordinaryskilled in the art that the form of software is dependent on, forexample, requirements of a desired application, the environment it runson, and/or the desires of a designer/programmer or the like.

Embodiments of the present invention apply analysis to exerciseparameters recorded during or after execution of an exercise activity todetermine assessment parameters for providing feedback to the trainee(hereinafter, the “subject”) of the actual training statistics as wellas a predictive target of exercise parameters that will modify theparameters according to the subject's abilities. Although the followingdescription relates to an implementation of an embodiment of theinvention for use by the subject, it will be appreciated of course thatthe present invention could be implemented for use by a trainer, coach,or the like. Indeed, it is possible that embodiments of the presentinvention could be implemented on-line (suitable, for example, forInternet based remote coaching) or as a mobile application.

Turning initially to FIG. 1 there is shown a block diagram for a system100 according to an embodiment of the present invention. The system 100includes a processing device 102 such as a desktop computer, a lap topcomputer, a note book computer, a hand held computer, a programmedelectronic device equipped with a programmed or programmable controller(such as a microcontroller), a smart phone, a personal digital assistantor the like. It will thus be understood that the term “processingdevice” is intended to denote any type of device including a processorcapable of executing a set of software instructions to perform afunction. The processing device 102 includes a processing unit 103, amemory 104, a computer software program 106 resident in a first store ofinformation in the form of a memory 104, communications interface 108,input interface 110, display interface 112, mass storage device 114, andpower supply 116, however, it will be appreciated that any configurationis acceptable such that the functionality of accepting data inputs,processing data and providing outputs in any format can be accommodated,either within a hardware device or any “virtual” or “cloud” device orprocess-capable medium, hereinafter encapsulated by the term “processingdevice” or “device 102”. Hereinafter, all references to specificelements of the device are taken to also address any otherconfigurations of the processing system.

The memory 104 may be installed on-board the processing device 102 ormay be connectable or accessible to the processing device 102 via asuitable communications interface, such as communications interface 108.A suitable communications interface includes a universal serial bus(USB) interface. The memory 104 may include volatile (for example, RAM,DRAM, SRAM) or non-volatile memory, such as a ROM memory (for example,PROM, EPROM, EEPROM), or NVRAM (such as FLASH) or the like. One exampleof a suitable memory is a USB FLASH memory adapted to communicate withthe processing unit 103 via a USB interface.

The memory 104 is programmed with a set of executable instructions inthe form of the computer software program 106. For the purpose of thisdescription, references to the term “memory” are to be understood todenote the total memory available to the processing unit. The software106 will also include an operating system for controlling systemfunctions. Suitable operating systems will depend on the processing unit103 and would be well known to a skilled addressee.

In addition to storing the executable instructions, the memory 104 mayalso store a database 107. Alternatively, the database 107 may be storedon a remote device, such as a server, or a device which is accessible tothe processing device 102 via the or another communications interface108 which may support wired or wireless communications with a network118, such as the Internet.

The communications interface 108 may thus include a wired interface suchas a USB, Ethernet or the like, or alternatively may include a wirelessinterface such as a Wi-Fi interface, a Bluetooth interface, or the like.Other suitable communications interfaces would be known to a skilledaddressee.

The communications interface 108 may support data communication whichallows the database 107 to be accessible to the processing device 102via a cloud. In this respect, the database 107 may include multipledatabases distributed across a plurality of network accessible devices.

The database 107 stores information for one or more exercise activities,such as weight training type exercises. The stored information includesload profile information (LP) for one or more exercise activities as theapproximate “load” of the weight on the working muscle(s) (that is, themuscle intended to be exercised by the exercise activity) at fourdistinct phases of the exercise, expressed ascomponents|Eccentric|Pause|Concentric|Pause| where the elements areeither 1 or 0 (that is, 1111, or 1011, or 1011). The load profile willbe described in more detail following. In some embodiments, the database107 also stores additional information for the one or more exerciseactivities such as the weight component lifted as a ratio of the weightloaded on the machine or bar (for example, 10%, 50%, 100%, 150%) as aresult of leverage, cams, or angle of lift versus angle of incidencewith gravity, the inherent and “minimum” weight as a result of theweight of the machine (for example, the foot plate of a leg pressmachine), and the component of bodyweight lifted in the exerciseactivity (for example, 10%, 50%, 100%, 150%). In the case of squatswhere part of the body (lower legs) are supported by the floor and notpart of the lifting load on the “working” muscles, or in the case ofchinning where the arms are supported by the chinning bar and the weightlifted is therefore the remainder of the body and any weight attachedvia a weight belt or otherwise held by the subject. Any variations ofthe above may apply and in some cases, multiple components will apply(for example, inclined hack squats where there is a minimum weight ofthe machine (the slide and shoulder pad supports), a percentage of thebodyweight lifted as with squats, and the addition of these two weightsis effectively reduced by the angle of the machine such that it is notdirectly aligned with the force of gravity. Thus, for example, the totalweight (wt) may be expressed as:

{[machine slide weight]+[added weight]+% bodyweight}×[% Angle gravitycomponent]

Furthermore, the database 107 also stores additional information in theform of a repository of historical training (exercise activities)records for exercise activities which have been executed by a subject toenable recall and analysis either for comparison purposes or forprocessing by the processing device 102 to generate a set of traininginstructions for executing the exercise activity based on priorexecution information.

Referring again to FIG. 1, the display interface 112 may include aconventional display screen, such as an LCD display but may also be anyform of display medium including hologram or “virtual” screens. Theinput interface 110 may include a keyboard, track-ball, touch-screen,mouse pointer, keypad, audio input, or “virtual” position sensor or thelike. Suitable input interface devices would be well known to a skilledaddressee.

In the present case, the input interface 110 provides a receiving meansfor receiving a plurality of exercise parameter values for an executedexercise activity into the processing device 102, and/or a means forproviding output information. For example, in some embodiments the userinterface 110 may provide:

-   -   input fields to record required data to analyse executed        exercise activities; and    -   an output display (such as a “dashboard”) of the exercise        activities executed to date and mechanism for prescribing new        targets for future exercise activities.

The input interface 110 may include an interactive graphical userinterface (GUI) which permits a subject to, for example, enter andselect parameter values for an exercise activity. Other suitable inputoptions may include sensor devices (for example, accelerometer(s), orstrain gauges) adapted with communication means, or specifically builttraining apparatus (with in-built detectors and data transmissionsystems) which are adapted to communicate data to an input device suchas a smart phone, tablet, laptop computer, desktop computer, wrist watchbased device, or other intelligent device, via a suitable wired orwireless interface. Such sensor devices may include devices which areworn by the subject during the exercise activity. The sensor devices maycommunicate with an input device via a suitable wired or wirelessinterface, or may feed data directly to a communications network or to aprocessing device locally or via the cloud.

The plurality of exercise parameter values will include informationrepresenting an execution profile (EP) for an executed exerciseactivity.

The processing unit 103 processes a plurality of received exerciseparameters values for an executed exercise activity, includinginformation representing the Load profile (LP), and possibly otherinformation, to determine one or more assessment parameter values forassessing the executed exercise activity. The processing unit 103 thenretrieves from the database 107, information representing the exerciseparameters, which may include the ratio of weight lifted, % bodyweightlifted, inherent machine weight, load profile (LP) for the executedexercise activity, and processes the plurality of exercise parametersvalues including the execution profile (EP) and the informationrepresenting the load profile (LP) to determine one or more assessmentparameter values for assessing the executed exercise activity.

FIG. 2 shows a flow diagram for a method according to an embodiment ofthe present invention. As shown, the illustrated method involvesreceiving, at step 202, a plurality of received exercise parametervalues for the executed exercise activity, wherein the information forthe executed exercise activity includes information representing theexecution profile (EP).

In embodiments in which the exercise activity executed by a subjectincludes a resistance training exercise activity involving one or moreweights (wt), one or more of sets (s) including one or more repetitions(R), and a total activity time (T_(e)), the plurality of exerciseparameter values will also include:

-   a) a set parameter value (n) representing the number of the one or    more sets (s) for the resistance training activity;-   b) a weight parameter value (wt_(s)) for the weight associated with    each rep within a set (s) where s=1 to n;-   c) a repetition parameter value (R_(s)) representing the number of    one or more repetitions in a s where s=1 to n; and-   d) a total activity time parameter value (T_(e)).

After the exercise parameter values for the executed exercise activityhave been received into the processing device 102, informationrepresenting the load profile for the executed activity is thenretrieved, at step 204, from the database 107. The plurality of exerciseparameters values and the information representing the load profile isthen processed, at step 206, to determine one or more assessmentparameter values for assessing the executed exercise activity.

The step of processing a plurality of exercise parameters values and theinformation representing the load profile will now be explained in moredetail.

As explained above, the load profile information includes informationwhich identifies the phases of the exercise activity during which amuscle targeted by the exercise activity is intended to perform work,and/or contribute to stress on the muscle. On the other hand, theexecution profile information includes information which relates to theduration of each phases of the exercise activity, as measured duringexecution of the exercise activity, irrespective of which phase wasintended to contribute to stress for that exercise activity.

In this respect, a resistance training type exercise activity involvingmanipulating (such as lifting and lowering) a weight (wt) for a setincluding a number of repetitions includes four phases of a repetition,namely an eccentric phase, an eccentric-pause phase, a concentric phase,and a concentric-pause phase, with each phase having an associatedduration. In an embodiment of the present invention, the load profilemay be represented as a binary digit code or “mask”, including valueswhich identify the phases of an exercise activity that are intended tocontribute to stress for that exercise activity. For example, the loadprofile (LP) may be expressed in a general form as:

LP=[d ₁ ,d ₂ ,d ₃ ,d ₄]

where:

-   -   d₁=value for indicating whether the eccentric phase is intended        to contribute to stress    -   d₂=value for indicating whether the eccentric-pause phase is        intended to contribute to stress    -   d₃=value for indicating whether the concentric phase is intended        to contribute to stress    -   d₄=value for indicating whether the concentric-pause phase is        intended to contribute to stress

It will of course be appreciated that the load profile (LP) may beexpressed in a different form.

With reference now to an example shown in FIG. 3, the load profile for a“squat” requiring an eccentric phase duration, an eccentric-pauseduration, a concentric phase duration, and no concentric-pause phaseduration the LP may be expressed, using the above-described generalform, as:

LP=1 1 1 0

The above expression applies if the exercise activity is performed suchthat the knees are locked at the top of the movement and the load iseffectively removed from the working muscles (primarilyquadriceps)—which may be referred to as “lockout squats” in the databasefor example. However, if the exercise activity is performed such thatthe knees are not locked and the load is always present on thequadriceps, the exercise may be referred to as “non-lockout squats”, inwhich case the load profile may be expressed, using the above-describedgeneral form, as:

LP=1111

In an embodiment of the present invention, the subject undertaking theexercise activity may be able to select the appropriate LP expressionfor the exercise by selection of the respective exercise activity fromthe database.

In an embodiment, the execution profile information may represent asequence of values, wherein each value represents a duration of one ofthe exercise phases of the exercise activity. For example, the executionprofile (EP) may be expressed in a general form as:

EP=[t ₁ ,t ₂ ,t ₃ ,t ₄]

where:

-   -   t₁=the duration of the eccentric phase    -   t₂=the duration of the eccentric pause phase    -   t₃=the duration of the concentric phase    -   t₄=the duration of the concentric-pause phase

In practice, the duration values t₁, t₂, t₃, and t₄ may be recordedduring execution of an exercise activity using an suitable means, suchas a stop watch. Verification to check approximate accuracy of theestimate may be achieved by the easier method of timing a set (forexample, if the estimated/targeted profile is 1110 and each repetitionis 10 seconds in duration, the total duration of the set is 30 seconds).The duration values may then be input into the system at the completionof the exercise activity for analysis by the system.

It is possible that in some embodiments, the duration values t₁, t₂, t₃,and t₄ may be automatically recorded by a sensing device worn by thesubject or attached to a machine, or incorporated in an exerciseapparatus being operated by the subject. Such a sensing device mayinclude, for example, a wireless sensor. Examples of a wireless sensorinclude a band or strap incorporating a sensor which is worn on, forexample, the wrist of the subject to detect movements during exerciseactivities such as a bench press, pull-downs, bicep curls, tricepextensions. In another example, the sensing device may be worn as awaist band, or as a necklet during chin-ups, or relocated to the ankleduring leg curls or leg extensions.

In other words, a sensing device would preferably be placed on a part orparts of the body involved with a primary range of movement targeted bythe exercise activity, for example, by placing the sensing device on thewaist (or worn on the torso) during chin-ups as opposed to the wrist,since the wrist does not move during chin-ups whereas the waist or torsoarea is involved with the primary or target range of motion used tostimulate the working muscles (latissimus dorsi). Similarly, the sensingdevice could remain on the wrist during squats, for example, since thewrist typically remains on the barbell at neck level and is thereforesubject to the full range of up/down motion during the exerciseactivity. If the subject was performing leg presses however, the strapwould be relocated to the ankle or the weight plate, or in the case of acable-weight machine, the device could be attached to the weight stack.Indeed, in most instances where a cable-weight stack is used, the weightstack may provide a preferable location for the sending deviceregardless of the body movements, as the weight stack is subject to thetrue range of motion.

In some embodiments of the present invention, the activity database forthe system may include reference data for interpreting sensor dataobtained from a sensing device attached to the weight stack, including:

-   -   a) direction of movement of the weight stack that corresponds to        the subject's concentric/eccentric movements; and    -   b) adjustment of the actual weight borne by the subject        according to, for example, a pulley configuration of the        machine, i.e. a single pulley means a 1:1 ratio of the weight on        the stack and the weight lifted, whereas a two-pulley        arrangement generally means that the weight lifted is half that        on the stack etc.

Embodiments of the present invention preferably access an activitydatabase which is indexed to identify the above criteria, and for thesubject and/or coach to make adjustments in the interpretation of sensordata if necessary.

As an example of tempo and load profile, the execution profile for a“squat” including an eccentric phase duration of 2 seconds, aneccentric-pause phase duration of 0 seconds, a concentric phase durationof 1 second, and a concentric-pause phase duration of 2 seconds may beexpressed as:

EP=2 0 1 2

According to embodiments of the present invention, processing the loadprofile and execution profile information involves determining a singlevalue indicating the total duration of the phases intended to contributeto stress during the execution of the exercise activity.

In the present case the single value may be considered as the actual“total time under tension” (TUT) resulting from the identified phases.In other words, in some embodiments, processing the load profile (LP)information and execution profile (EP) information provides a singlevalue indicating the accumulated time under stress contributed by phasesidentified by the load profile information during execution of theexercise activity. In the present case, the processing determines asingle value as the total time under tension caused by the identifiedphases as the sum of the products of the values of the load profile andthe values of the execution profile. Accordingly, the TUT for each repin a set may be expressed as:

TUT _(r) =t ₁ ·d ₁ +t ₂ ·d ₂ +t ₃ ·d ₃ +t ₄ ·d ₄

or more generally for the set as:

${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}\; {t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$

In other words, for an exercise activity involving n sets (s), a TUT maybe determined for each set as the average TUT per repetition.

Hence, in the example shown in FIG. 3:

-   -   LP=1 1 1 0    -   EP=2012

TUT=2.1+0.1+1.1+2.0=3

FIG. 4 includes further examples of TUT derivations for differentexercise activities.

In another embodiment of the system, exercise parameters may bedetermined for each individual repetition rather than using an averageof the TUT per repetition. Such an approach may provide additional datain embodiments of the system where performance over time of each set isof interest.

It will also be appreciated that it is possible that exercise activitiesmay be performed in ways to modify the load profile. For example, with a“barbell row” type exercise activity involving manipulation of a weight(wt) to exercise the latissimus dorsi muscle, if the weight is raisedand lowered in controlled fashion, then TUT has an impact, in terms ofthe contribution to stress, during the “up” (that is, the concentricphase) and “down” (that is, the eccentric phase) phase. Furthermore,there is also an impact at the top if the weight is held at thecontracted-pause phase, and also at the bottom since the subject isstill supporting the weight under stretch in the eccentric-pause phase.However, if the weight is “cheated up”, the TUT will be reduced sincethe momentum carries the weight up. In this example, this effect occursbecause most of the weight is borne by the lower back in “throwing theweight”.

Nevertheless, when the weight is lowered, there is an impact during the“down” phase if the weight is lowered under control and furthermore ifthe weight can be held or paused at the top there will also be acontribution to stress at this point. On the other hand, if the weightis “cheated” up and down, and not held in the top position, the loadprofile is effectively 0100 since there is just the load under stretchat the bottom position (that is, the eccentric-pause phase), since thelower back does most of the work at the start to ‘throw’ the weightthrough the up phase, thus bypassing the muscle targeted by the exerciseactivity (that is, the latissimus dorsi muscle) and not contributing tostress of the muscle intended to be exercised by the activity.Variations for executing the exercise may be included in the database.For example, the activity database may include “Barbell row 1011”,“Barbell row 1010”, “Barbell row—cheat 0100” or any other means ofconveying the exercise performance style and hence the actual loadprofile.

To explain further, embodiments of the system may provide an option forexercise activities to be performed with a stipulation of continuoustension/load. To clarify the advantage of specifying this variation instyle and load profile:

-   a) exercises activities may have the possibility of reducing or    eliminating the load on the muscle at some point of the movement    (for example, squats with the knees locked out enable the quads to    relax (LP=1110); bicep curls with the arms hanging by the sides    enable the biceps to relax, and if the weight is curled to the top    such that the forearms are vertical or past vertical, the biceps    again are relieved of stress (LP=1010). Hence, the load profile may    be specified to take into account these variations:    -   a. Curls to arms “hanging” and up to vertical: LP=1010;    -   b. Curls to arms “hanging” but short of vertical: LP=1011;    -   c. Curls short of full hang or with shoulders slightly flexed as        in a “preacher” curl, and stopping at or past vertical: LP=1110;        and    -   d. Curls short of full hang or with shoulders slightly flexed as        in a “preacher” curl, and stopping short of vertical: LP=1111.-   b) Similarly, each exercise activity may be performed so as to    maintain stress on the working muscle(s):    -   a. Squats stopping short of knee lock-out: LP=1111;    -   b. Bench press stopping short of elbow lockout: LP=1111;    -   c. Tricep lying “EZ extension” stopping short of elbow lockout:        LP=1111;    -   d. Leg curl stopping short of resting weights on the stack:        LP=1111; and    -   e. Dumbbell Oyes stopping short of full vertical: LP=1111.

Embodiments of the present invention may cater for these variations, andpermit the system to determine which variation was used for eachexecution of an exercise activity.

In some embodiments, the variation may be stipulated by the subject orhis/her coach (i.e. by specifying in a recording device which option ofperformance was used). In other embodiments, the system may detect theoption by incorporating, for example, a sensor(s) which conduct a “fullrange of movement calibration” such that a full range of movement isperformed initially with either no weight or a light weight prior to theexecution of the actual exercise activity so the system can detect thefull eccentric and concentric positions and possibly automaticallyassign, for each repetition, the appropriate load profile.

For example, prior to executing a barbell curl, the subject may executea full range repetition. During the execution of the full rangerepetition the system may detect the 0 and 180 degree positions, then,as the subject executes the barbell curls and the first repetition (“Rep1”) stops at 75 degrees, the concentric pause of the LP is assigned as“1”, but for the second repetition (“Rep 2”), the arm is extended to avertical position, in which case the concentric pause of LP is assignedas “0”. In this way, the product of the execution profile and the loadprofile for the executed exercise activity may be determined as:

EP×LP=[2012],[1011]=[2012]TUT=5; and  Rep 1:

EP×LP=[2022],[1010]=[2020] and TUT=4.  Rep 2:

In other embodiments, the system may estimate the load profileinformation throughout the range of movement using, for example, ascalar or percentage as opposed to simply assigning a “1” (on load) or“0” (no load). For example, with bicep curls, obtaining the angle of theforearm at the top of the range of movement may allow for the loadprofile to be estimated as having a value less than 1 (for example, 0.5)to indicate that the bicep is being stressed at 50% during that phase.Furthermore, it is possible that the load profile and duration could besampled at various points over the range of movement to further improveaccuracy.

To further explain load profile, execution profile and impact profile,with a “squat” type exercise activity intended to exercise the quadricepmuscles, the “standard” load profile may be “1110” since at the top(that is the concentric-pause phase) of the movement, the loaddisappears from the quadriceps if the knees are locked. Hence, if asubject executes an execution profile of 1016, that is, a 1 secondeccentric phase (that is, during the down movement), no pause at thebottom, a 1 second concentric phase (that is, during up movement) andthen rests (with knees locked out) for 6 seconds at the top before thenext repetition, according to the present invention the actual TUT isnot 8 seconds, but is instead 2 seconds.

Hence if the exercise activity includes 8 repetitions in a set, the setwill have a total duration of 58 seconds (noting that the duration isnot 64 since on the last rep the subject will effectively ‘rack’ theweight for 6 seconds). However, the effective TUT (that is, the actualworking TUT) as determined by the method according to the presentinvention is 16 seconds.

Turning now FIG. 5 there is shown two examples (“Example A” and “ExampleB”) of a subject's execution of a bicep curl exercise activity. In“Example A” the subject's arm 500 is moved to ensure constant load onthe biceps 502, at the start the elbow 504 is slightly forward so thebiceps 502 work from the start, and at the top of the movement, theforearm 506 is only just above parallel so the biceps 502 are workinghard to contract. On the other hand, in Example B, the elbow 504 isretracted at the start in cheat style meaning that half of the work isalready done without actually loading the biceps 502 simply by bringingthe elbow forward. (ref. position B2) Then at the top of the movement,the elbow 504 is thrust forward and the forearm is vertical (refposition B3), or even in some cases, the weight is allowed to ‘rest’(ref. position B4). In other words, Example B is a less effective way toperform a curl but is unfortunately used by many subjects to use veryheavy weights in the belief that they are making progress, even thoughthis approach is less effective.

In the case of Example B, the execution profile information may berepresented as:

EP=1,0,0.5,0

In other words, no “load” at the start or end, and a low “load” duringthe “up” concentric phase due to ‘throwing’ the weight. In some cases asubject may allow the weight to ‘drop’ in the eccentric phase, as wellas bringing the elbows back again so the repetition is not completedproperly. In this example, the execution profile information may berepresented as:

EP=0.5,0,0.5,0

Having determined the TUT, embodiments of the present invention thenprocess the TUT with the plurality of exercise parameter values for theexecuted exercise activity to determine one or more assessment parametervalues for assessing the executed exercise activity.

In an embodiment, processing the plurality of exercise parameter valuesto determine one or more assessment parameter values for assessing theexecuted exercise activity includes determining:

-   -   a) a work volume parameter value for the executed exercise        activity (W);    -   b) a work intensity parameter value for the executed exercise        activity (W_(i));    -   c) a stress intensity parameter value for the executed exercise        activity (S_(i)); and    -   d) a hypertrophy factor parameter value for the executed        exercise activity (H_(f)).

It is possible that other assessment parameter values may also bedetermined.

As is shown in FIG. 6, the weight (wt) may include part of the subject'sbodyweight. For instance, with squats, about 80% of the body is alsolifted (as the lower leg region of each leg is ‘supported’ on the floorand is not lifted, and the upper leg region of each leg is partiallysupported at the knee joint). With chin-ups, about 90% of the body islifted (as the hands and forearms are “locked” to the bar and are notlifted). Hence, it is possible that for some exercise activities theweight (wt) may be a proportion of the subject's body weight.

Similarly, with some exercises, the weight lifted (wt) is not the“actual” or apparent weight on the bar. For instance, with a leg pressexercise activity on a 45 degree incline, only 60% of the weight loadedis actually transferred to the subject. In other words, the effectiveweight is a proportion of the total weight manipulated by the subject.There may also be part of the weight of the apparatus to take intoaccount (i.e. a minimum weight such as the weight of the slide or baseplate etc.) before the % weight calculation is applied.

For a resistance training type exercise activity involving manipulatinga weight (wt) for a number (n) of sets (s) including multiplerepetitions (R) over a duration (T_(e)), the assessment parameter valuesmay be determined as follows.

First, a work volume (W) parameter value may be determined as:

$W = {{\sum\limits_{s = 1}^{n}W_{s}} = {\sum\limits_{s = 1}^{n}( {R_{s} \cdot {wt}_{s}} )}}$

where:

-   -   W_(s): the work volume for set s where s=1 to n    -   R_(s): the number of repetitions in set s    -   wt_(s): the weight manipulated in set s.

Then, by application of the TUT determined for each set, a stress (S)value may be determined as:

S=R ₁ ·wt ₁ ·TUT ₁ +R ₂ ·wt ₂ ·TUT ₂ + . . . +R _(n) ·wt _(n) ·TUT _(n)

which may be further expressed as:

S=Σ _(s=1) ^(n) R _(s) ·wt _(s) TUT _(s)

where:

-   -   TUT_(s)=the TUT value for set s where s=1 to n

Having determined the stress, the stress intensity assessment parametervalue may then be determined as:

$S_{i} = \frac{S}{T_{e}}$

and the work intensity parameter value as:

$W_{i} = \frac{W}{T_{e}}$

The hypertrophy factor assessment parameter may be determined as:

H _(f) =S _(i) ·W

An example of the determination of the assessment parameters isdescribed following to assist the reader in understanding an approachfor determining the assessment parameter values.

Example 1

A subject completed a bench press exercise activity with exerciseparameters as listed in table 1 over a total exercise activity durationof 510 seconds.

TABLE 1 Drop Execution Primary Set Profile Set (wt · R) (wt · R) (EP) 110 × 60  1010 2 8 × 70 1010 3 7 × 70 1010 4 9 × 60 5 × 50 1010 5 10 ×50  6 × 40 1110

The exercise activity was performed as a bench press in which nocomponent of subject's bodyweight was lifted. In this case, the actualweight loaded was the weight lifted (including the bar), and the totalwork volume was determined as:

W=10×60+8×70+7×70+9×60+5×50+10×50+6×40=3,180 kg-reps

Since the execution profile for sets 1 to 4 of the exercise activity was1010 and the load profile for bench was 1110 (with elbow lockout), theTUT for sets 1 to 4 was determined as:

TUT=t ₁ ·d _(i) +t ₂ ·d ₂ +t ₃ ·d ₃ +t ₄ ·d ₄

TUT=1.1+0.1+1.1+0.1

-   -   TUT=2 seconds per repetition

For set 5, the TUT was determined as:

-   -   TUT=3 seconds per repetition

The stress (S) was determined as:

$\mspace{20mu} {S = {\sum\limits_{s = 1}^{n}{R_{s} \cdot {wt}_{s} \cdot {TUT}_{S}}}}$S = 10 × 60 × 2 + 8 × 70 × 2 + 7 × 70 × 2 + 9 × 60 × 2 + 5 × 50 × 2 + 10 × 50 × 3 + 6 × 40 × 3  S = 7, 100  kg-rep-secs

The work intensity (W_(i)) was determined as:

$W_{i} = \frac{W}{T_{e}}$ $W_{i} = \frac{3180}{510}$W_(i) = 6.24  kg − r/sec 

Stress Intensity:

$S_{i} = \frac{S}{T_{e}}$$S_{i} = {\frac{7100}{510} = {13.92\mspace{14mu} {kg}\text{-}r\text{-}s\text{/}s}}$

Hypertrophy Factor:

H=S _(i) ·W

H _(f)=44,271 units

In this example, the determined assessment parameter values were thus:

W S W_(i) S_(i) H_(f) 3,180 7,100 6.24 13.92 44,271

It is to be appreciated that additional assessment parameters may alsobe determined. For example, embodiments of the present invention mayalso determine:

-   -   Max weight: the maximum of the weight used for any of the        sets=Max(_(t)″)    -   Total repetitions: the sum of all the reps of all working sets        and drop sets, which in this example=55=Σ₁″(r_(n)+d_(n))    -   Average weight: the average weight per rep, hence Total        Work/Total reps, which in this example=57.8 kg=W/R    -   Total sets: sum of all working sets (where drop sets are part of        a primary set), which in this example=5    -   Drop sets may also be tallied separately. In this example there        are 2 drop sets, hence the work and stress intensities were        achieved with the configuration of 5 sets plus 2 drop-sets    -   Average repetitions per set: total reps/total sets which in this        example=55/5=11=R/S (also=W/average weight/rep)

The above example provides assessment values which may permit a subjectto readily compare the effectiveness of different exercise activities interms of their effectiveness on stressing a targeted muscle, as isexplained with reference to the below example.

Example 2

A subject completed a bench press exercise activity with exerciseparameters as listed in table 2 over an activity duration of 600seconds.

TABLE 2 drop Set primary set1 EP LP TUT Set 1 14 × 50 1010 1110 2 Set 210 × 55 1010 1110 2 Set 3  9 × 55 1010 1110 2 Set 4  9 × 55 5 × 40 10101110 2 Set 5 10 × 50 4 × 40 1110 1110 3

The following assessment parameters were then determined using theprocess described with reference to Example 1, which for clarity omitsother assessment parameters such as the no. of sets, drop sets, maxweight, avg weight per rep, total reps, and avg reps per set.

W S W_(i) S_(i) H_(f) 3,180 7,100 6.24 13.92 44,271

The assessment parameter values determined in Example 2 were thencompared with the corresponding values determined for Example 1 asfollows:

Parameter Example 1 Example 2 H_(f) 44,271 35,443 S_(i) 13.92 11.43W_(i) 6.24 5.17 W 3,180 3,100

The comparison indicated that the exercise activity described in Example1 was more effective than the exercise activity described in Example 2in terms of stressing the muscle intended to be exercised by theexercise activity for the goal of hypertrophy or conditioning.

In this manner, the present invention may assist a subject to identifypermutations of each exercise activity to achieve maximum effectivestress to elicit hypertrophy (or performance response) in each of theirgiven muscles. The output instructions may provide a more ideal targetfor subjects seeking growth (i.e. H_(f), S_(i), W_(i)) than thetraditional methods which are typically pre-set or “prescribed”performance parameters that do not take into account a subject's uniquephysiology.

Other targets may also be “set” such as maximum work performed or workintensity for the purpose of improving performance in a sport or forincreasing calorie expenditure.

Embodiments of the present invention may store assessment parametervalues for subsequent analysis to generate instructions for one or moreexercise parameters for the future execution of the exercise activity bythe subject, such as in a periodised training program. The instructionsmay provide a subject with plural program selections or options basedaround their identified ideal performance parameters for each muscle andthus assist the subject in aiming for the new ideal targets viaperiodised training, to avoid over-training, as explained in furtherdetail as follows.

Embodiments of the present invention may analyse, for example, storedassessment parameter values to identify “peak” exercise activitieshaving, for example, the highest combination of stress intensity andwork and set a new target or objective based on these identified “peak”exercise activities. The subject may then work towards a new target bydeveloping strength and work capacity “either side” of the target whilstalso allowing recovery between exercise activities. Hence the exerciseactivities may, for example, “cycle” a load between higher weights andlower volume (and stress) and lighter weight and higher volume, and alsocycle periods of higher stress intensity but lower volume (hence lowerH_(f)) to stimulate the muscles to develop, whilst reducing thelikelihood of physically or psychologically “burn-out”.

The meso-cycles may be determined by calculating variations about the“target” H_(f) exercise activities and varying the loads to avoidover-training as well as provide the necessary stimulus for thesubject's muscles to achieve the target. Embodiments of the presentinvention may thus provide a guide, in the form of a set ofinstructions, to cycle loads and periodise exercise activities, and mayalso provide guidelines on how to adjust the exercise activitiesinstinctively. The periodisation may be determined via suitablealgorithms.

An embodiment of the present invention may also thereby providestatistics and guidelines for the subject in relation to the exerciseparameters over longer periods of time such as micro-cycles,meso-cycles, and macro-cycles. In this manner, the subject will alsogather performance and response data for the parameters for any givenmuscle that have meaning over the longer term, such as total work andanalysis of the periodic stress and stress intensity over the longerterm.

An example implementation of a method according to an embodiment whichgenerates instructions for one or more exercise parameters for thefuture execution of the exercise activity by the subject, such as a newperformance target or a strategic variation in a periodised trainingprogram to reach the new target, is described following.

Example 3

Exercise Activity: Incline bench

-   -   7 sets×15 reps×50 kg, TUT 3 s/rep Rest 86 s/set

Alternative exercise activity instructions recommended by system:

-   -   10 sets×6 reps×85 kg; TUT 2 s/rep, Rest 50 s/set

Both of the above exercise activities equate to the same hypertrophyfactor H_(f) (that is, Stress intensity×Work volume). However, in thiscase the system has identified an alternative target for the subjectthan the traditional “maximum weight” or “max weight for 8 to 12 reps”.By using the present invention, the subject may identify exerciseactivity combinations which improve the likelihood of the subjectachieving the target work capacity, stress intensity and hypertrophyfactor that the system determines for him/her.

In order to achieve this new target, the subject may train with varyingparameters determined and/or predicted by the system and designed toultimately develop the aspects of the muscle to achieve thatperformance, that is, a combination of strength stamina, and workcapacity.

Advantageously, specifying “equivalent” targets effectively identifiesthe preferred parameters for peak performance in an iterative guidancemanner.

The varying parameters mentioned above may be determined by the systemin a ‘meso-cycle’ program which will stress the muscles around anoptimum operating mode as well as providing ‘detraining’ sessions toprevent burn-out. The system may also aim to have the subject achievepeak stimulation not just per workout but also per week, month, andmeso-cycle in order to achieve and advance the peak capacity as quicklyas possible.

Embodiments of the present invention may analyse recorded exerciseactivity information to determine one or more exercise activities whichinvolve a slightly increased H_(f), that also match the subject'scapabilities. For example, knowing that the subject can manage anaverage of 11 reps with 58 kg, provides a guide to the weights possiblewith other anaerobic repetition ranges.

The historical database of executed exercise activities may categoriseexercise activities that have similar effects on the musculature so thatexercise activities can be compared as opposed to simply comparing onlysessions of the exact same exercise activity. For example, the databasemay have similar ID coding for flat barbell bench press and flatdumbbell bench press. This approach may enable comparisons of allworkout histories of these exercise activities.

Example 4

In this example, the average weight for an exercise activity wasdetermined as 58 kg for 12 reps. The system may determine a valueexpressing the average weight in terms of a proportion, or percentage of1 RM (that is, the subject's one repetition maximum weight for theexercise activity) by indexing, for example, a table in the systemdatabase “%1 RM vs Reps”. Additional repetition ranges, for differentweights, may then be estimated from their typical %1 RM values as shownin FIG. 7. This type of table may be calculated for each exerciseactivity when predicting new target workouts.

Embodiments of the present invention may employ, for example, anexisting or standard % 1 RM vs Reps reference table, and then furtherrefine it over time for each subject in response to the subject'sexecution of an exercise activity. For example, a standard table mightsuggest that 8 reps is typically possible with 60%1 RM, and byextrapolation that the 70%1 RM weight may be determined and, by reverselook-up of the table, 6 reps should be possible. However, for aparticular subject in this exercise activity, the subject maydemonstrate, on execution of the exercise activity that only 5 reps arepossible. In this case, the table may be modified for subject andexercise (over time with the gathering of data), and hence for futurepredictions when determining new targets.

It is to be understood that refinement of the % RM vs Reps table is notessential. Indeed, it is anticipated that “industry standards” referencetables will be suitable as a guide since, in use, the system calculateswhat the subject actually achieves, and the subject (or theircoach/trainer) learns with experience which targets are reasonable.

In the present case, there are two tables:

-   -   Reps (R) vs %1 RM (this is a “fixed” reference table)    -   Reps® vs Weight (W) (this table is calculated for each        workout/exercise activity on the basis that for the average        weight used vs average reps, the corresponding %1 RM for those 4        reps is known and the expected weight for every other %1 RM can        be determined)

Hence for the table of Reps vs %1 RM, weight values can be determinedfor each Rep value by retrieving the %1 RM for the actual reps andpro-rating the values for all other rep values.

That is, for each rep option to be offered, the above tables can bereferred to since the predicted reps have been determined based on theactual reps and weight lifted and by relating them to the R v %1 RMtable. By way of example, the rep options may be as follows:

Reps/set 12 20 15 10 8 6 4

For a particular exercise activity (or average of parameters for anexercise activity), an embodiment of the present invention may determineand/or predict the likely weights and repetition ranges. The subject, orthe system, may then program an increase of H_(f) or other keyparameters, such as:

-   -   H_(f) increase (or decrease) %    -   S_(i) inc % with W inc % (that is, increase/decrease Si with an        increase/decrease W)

It is to be appreciated that the targeted reps/set may not necessarilybe limited to the repetitions used in the example.

Alternatively, instead of reps/set, a range of target weights may beused. For example, if the subject's past performance of an exerciseactivity included 8 reps per set with 50 kg, then targets may bedetermined from the predicted 1 RM and working on %1 RM as follows:

wt/set 80 kg 70 kg 60 kg 55 kg 50 kg 45 kg 40 kg

Based on the above information, the system may determine a plurality ofinstructions for executing an exercise activity which meet the trainingcriteria, as follows:

-   -   a) Using the various repetitions and the weight/% RM table (ref.        FIG. 7), the system determines the weight (wt) for each        anaerobic repetition. e.g. 58 kg for 12 reps.    -   b) Then, for a target work volume (W), the system determines how        many repetitions are required for each weight, by indexing the        weight/% RM table (ref. FIG. 7).

c) Then, knowing the repetitions possible with each weight, the systemdetermines how many sets will be required to achieve the target workvolume for each of a plurality of weight and repetition combinations.For example, if W=3600

$S = \frac{W}{R \cdot {wt}}$ $S = \frac{3600}{12.58}$ S = 5.2

Further examples are shown below.

Reps/set 12 20 15 10 8 6 4 Est wt to = W 58 44 52 69 81 92 104 Repsrequired 63 83 70 52 45 39 35 # sets 5.2 4.1 4.6 5.2 5.6 6.5 9

-   -   d) From the above determined values, the durations of each set        are then calculated using the actual TUT of the execution        profile. Note that when determining the initial performance        parameters, the load profile was considered against the        execution profile to determine an actual load stress. In working        backwards to arrive at a target elapsed time for the exercise        activity, the predicted duration of each set is required in        order to guide the trainee on the required rest between each        set. This of course assumes that the trainee performs the reps        according to the targeted working TUT—i.e. if the trainee        selects an option of weights and reps and a rep TUT of 5        seconds, the system is guiding the trainee such that those 5        seconds are all stressing the muscle (according to the load        profile). Hence, if the exercise has a load profile of 1110, and        the target TUT is 5 seconds, the reps would be performed as        2120, or 3020 etc. Both of these have a working TUT of 5        seconds. However, if the trainee does not achieve the target or        makes a mistake, and for instance performs the reps as 1013 (in        other words, “cheating” the movement)—this will show up in the        program reports as the trainee will enter their actual TUT as        1013 (particularly if a sensor is used as it will record the        phases exactly) and see that the calculated S, S_(i), and H_(f)        are below target (due to the Working TUT only being 2). They        will learn and correct this next time.    -   e) Various per rep values based on the load profile information        for the exercise activity, as follows:

Est Set TUT/set 12 20 15 10 8 6 4 2 24 40 30 20 16 12 8 3 36 60 45 30 2418 12 4 48 80 60 40 32 24 16 6 72 120 90 60 48 36 24

-   -   f) The total Work is known as it is a targeted Work volume. The        Work per set will depend on the weight and the repetitions from        the first table as follows:

12 20 15 10 8 6 4 Work/set 691 876 778 691 645 553 415

-   -   g) The Stress per set is then calculated, using the Work per set        and the TUT per set, as follows (each row corresponds to a        different working TUT):

12 20 15 10 8 6 4 Stress/set 1383 1752 1556 1383 1291 1106 830 2074 26272334 2074 1936 1659 1245 2766 3503 3111 2766 2581 2213 1659 4149 52554667 4149 3872 3319 2489

-   -   h) The total stress for the exercise activity is then determined        as Σ₁ ^(n) Stress_(n) as follows:

12 20 15 20 8 6 4 Total 7,229 7,229 7,229 7,229 7,229 7,229 7229 stress10,844 10,844 10,844 10,844 10,844 10,844 10844 14,458 14,458 14,45814,458 14,458 14,458 14458 21,688 21,688 21,688 21,688 21,688 21,68821688

-   -   i) The elapsed time T_(e) for the exercise (completion of all        sets) is then determined using the targeted Stress Intensity        (Stress/T_(e)).

12 20 15 20 8 6 4 Te 363 363 363 363 363 363 363 544 544 544 544 544 544544 725 725 725 725 725 725 725 1,088 1,088 1,088 1,088 1,088 1,088 1088

-   -   j) Hence for all set combinations and total Stress (S)        determinations above, the system then determines the T_(e)        required to achieve the target S_(i) for all permutations.    -   k) Finally, since the system has determined the TUT per rep, the        number of repetitions per set, and number of sets, it then is        able to determine the actual time lifting the weight. Further,        since the system knows the total elapsed time Te, and the number        of rest periods (#sets less 1), the system calculates the        average rest time between sets. (Te−total reps×TUTr))/(#sets−1)    -   l) For example,

(363  secs − (63  reps × 2  secs))/(5 − 1)(using  the  top  left  figures  from  the  tables). = (363 − 126)/4) = 237/4 = 59  seconds  rest  avaerage  per  set

Further examples of rest period determinations are shown below forcorresponding reps per set (columns) and working TUT (rows):

12 20 15 20 8 6 4 Rest/set 59 66 56 65 55 47 37 89 99 84 97 82 71 55 119132 112 129 109 95 73 178 198 167 194 164 142 110

-   -   m) The rest period is then calculated for each weight, rep, and        TUT variation. All of these permutations represent the same        target H_(f), or S_(i) and W combinations.

Having calculated the parameters outlined above, embodiments of thepresent invention then output the results in the form of a set ofinstructions for executing the exercise.

FIG. 8 shows one example of an output 800 for display. The illustratedoutput 800 provides a user-friendly presentation of the weight/rep/TUTand rest combinations to achieve a target H_(f), or W and S_(i).

Another element shown in the output shown in FIG. 8 is a preference toolof rest and TUT options to help the subject select an exercise parametercombination as may be done for example via intuitive periodisation orfor gaining more data about a muscles working capacity in order tobetter identify the optimum training parameters. For example, thesubject may have trained heavier last workout (with less reps andtherefore lower TUT per set) and therefore may wish to choose a workoutwith higher reps and longer TUT per set (as part of the meso-cycle andadaptation process). They may also have trained with longer rests andwant to shorten the rests to increase the intensity. In the illustratedoutput, the subject may therefore “tick”, in this example, the “Rest=31to 51” seconds option to highlight all exercise activities matching thatcriteria (as identified the “rest” column with “<<<<”)

The subject may then “tick”, in this example, the rest options “31-51”and “51-61” to identify rest periods 31 to 61 seconds, and highlightthose workouts (as identified in the TUT column with “<<<<” or by otherhighlighting means).

Further, where in the event that both criteria are met, the systemhighlights the indicators in both columns.

Some exercise activities may be possible for the subject and others maybe too challenging. However, through using the system it is anticipatedthat the subject will learn their limitations and will intuitivelyselect the combinations more likely to be achievable. In this way, thesystem “adapts” to the subject and finds their optimum training stylefor each muscle/exercise. This applies even over time of the subject'sperformance changes with age or other factors.

Filtering the Selection

In an embodiment of the invention, various options for the next workouttargets may be filtered for display to assist with the selectionprocess. For example:

-   -   a) A filter may be provided to filter the desired repetition        ranges. For example, 4 to 8; 9 to 12; 13 to 15; 16 to 25;    -   b) A filter may be provided to filer rest period options. For        example, 15 to 30 seconds; 31 to 60 seconds; 61 to 90 seconds;        91 and above seconds    -   c) A filter may be provided to filter may be TUT per set        options. For example, 5 to 16 seconds; 17 to 30 seconds; 31 to        59 seconds; 60 seconds and above    -   d) Or other combinations.

Drop Sets

An embodiment of the present invention may also include guidance forusing drop-sets in the target workouts.

This may be achieved as follows:

-   -   a) Calculating target options for various repetition schemes as        identified previously;    -   b) Selecting target workout parameters for an exercise activity,        for example: bench press; 7 sets×15 reps×50 kg, TUT 3 s/rep Rest        86 s/set;    -   c) Selecting an option to execute the exercise activity as a        “drop set”;    -   d) In an embodiment, the drop sets are determined by:        -   a. Revise the “primary” sets as P′=P sets×0.8;        -   b. Weights and reps for P′ remain unaltered of course as            these are known from history to be the subject's            capabilities—however the Work is now reduced and needs to be            restored by the drop sets;        -   c. Determine the Work from the P′×weights×reps=W−W′;        -   d. Determine the number of drop sets as P′/2 (hence the            total sets are increased by 20% of the original prescription            that is, if the initial set prescription P was 5 sets, total            sets will now be 6 (120% based on 80%+50% of 80%) such that            4 sets are now the primary (40% of 5) and 2 sets are drop            sets (50% of 4);        -   e. Determine the weight for the drop sets based on 80% of            the weight of the primary set. For example, if the primary            set is 50 kg as in this example, the drop set will be 40 kg;        -   f. Determine the Work in the drop-sets required to bring the            total work back to the original Work;            −W−(P′×wt×Reps×Sets×80%);        -   g. Determine the Work intensity, knowing the TUT for all            sets and the fact that the drop set is executed            theoretically as zero seconds from the primary set;        -   h. Note that Te (elapsed time for all 6 sets) will be the            same as the original Te since Work is now the same and            intensities are required to be the same;        -   i. Determine the duration of the drop sets (knowing the            number of reps and the TUT); and        -   j. Determine the revised rest between the primary sets,            knowing the set durations, the zero rest from primary to            drop sets, and the total exercise duration Te.

In another embodiment a system in accordance with the present inventionmay provide an option as above known as a “standard” drop set, or allowselection of a “customised” drop set criteria. A customised drop setcriteria may enable the subject or coach to pre-set the system withchoices of various combinations such as:

-   -   Primary sets P′=1, 2, 3, 4;    -   Or primary sets P′=80% P, 60% P, 50% P;    -   Drop set weight wt′=80% wt, 60% wt, 50% wt; and    -   The calculations in these cases are performed in similar fashion        as per the explanation for the current “standard” drop set        calculations.

Real-Time Guidance

In an embodiment of the invention, the system may perform thecalculations for the current exercise activity in real-time and providefeedback to the subject in real-time, or near real-time, on theirprogress to meeting their target.

For instance the system might indicate that based on the Work and timesthus far in the exercise activity and that only one set remains, that ifthe subject starts the set in 20 seconds and performs the desired repsin the specified form, the subject will achieve their target.

Furthermore, embodiment of the present invention display a total H_(f)score that will be achieved if the subject starts the last set in thewithin a particular duration (for example, 20 seconds), and then updatesthis score periodically (for example, every 5 seconds) and displays theupdated score against the previous best score.

Phase Timing Guidance (EP), Audible

In another embodiment of the invention, the system may provide an alertfor each phase of the exercise (for example, for each phase of thetargeted execution profile for each rep) to assist the subject with thetiming of the phases during the exercise activity. For instance, thesystem may provide an audible tome or “beep” at the start of eachrepetition phase, in other words, the eccentric phase, the eccentricpause phase, the concentric-phase, or the concentric-pause phase toindicate the tempo of an exercise activity. In this way, if the subject,for example, is in the eccentric phase and commences the concentricpause phase before the next “beep”, they know they are going too fast,etc.

In view of the above, it will be appreciated that the present inventionprovides a plurality of exercise activity instructions of varyingweight, reps, TUT, rest that all match a targeted Hf or Si and W.Furthermore, the present invention is expected to assist a subjectidentify exercise activities which match their preference when planningtheir next workout. Preferably, this process is accomplished in oneoperation for all exercises activities that the subject plans for thenext workout. The trainee can then select the preferred parameters foreach exercise by “ticking” the workout that matches their preferredcharacteristics.

The system then downloads this workout data to a printable area as wellas a field that can be exported to, for example, SMS or other e-mediaoutput or displayed.

It should be appreciated that the present invention can be implementedin numerous ways, including as a process, an apparatus, a system, or acomputer readable medium such as a computer readable storage medium or acomputer network wherein program instructions are sent over wireless,optical, or electronic communication links. It should be noted that theorder of the steps of disclosed processes may be altered within thescope of the invention.

Details concerning computers, computer networking, software programming,telecommunications and the like may at times not be specificallyillustrated as such were not considered necessary to obtain a completeunderstanding nor to limit a person skilled in the art in performing theinvention, are considered present nevertheless and as such areconsidered to be within the skills of persons of ordinary skill in theart.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips may be referenced throughout the abovedescription may be represented by voltages, currents, electromagneticwaves, magnetic fields or particles, optical fields or particles, or anycombination thereof.

Those of skill in the art would further appreciate that the variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. It should be noted that there are many alternative waysof implementing both the process and apparatus of the present invention.Accordingly, the present embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

Throughout this specification and the claims that follow unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood toimply the inclusion of a stated integer or group of integers but not theexclusion of any other integer or group of integers.

The reference to any background or prior art in this specification isnot, and should not be taken as, an acknowledgment or any form ofsuggestion that such background or prior art forms part of the commongeneral knowledge.

It will be appreciated by those skilled in the art that the invention isnot restricted in its use to the particular application described.Neither is the present invention restricted in its preferred embodimentwith regard to the particular elements and/or features described ordepicted herein. It will be appreciated that various modifications canbe made without departing from the principles of the invention.Therefore, the invention should be understood to include all suchmodifications within its scope.

Although a preferred embodiment of the method and system of the presentinvention has been described in the foregoing detailed description, itwill be understood that the invention is not limited to the embodimentdisclosed, but is capable of numerous rearrangements, modifications andsubstitutions without departing from the scope of the invention as setforth and defined by the following claims.

1-69. (canceled)
 70. A method of analysing a resistance exerciseactivity executed by a subject, the method including: receiving aplurality of exercise parameter values for the executed exerciseactivity into a processing device, the plurality of exercise parametervalues including information representing an execution profile (EP) forthe executed exercise activity; accessing a store of information toretrieve information representing a load profile (LP) for the executedexercise activity; and processing the plurality of exercise parametervalues and the information representing the load profile to determineone or more assessment parameter values for assessing the executedexercise activity.
 71. A method according to claim 70 wherein theexercise activity executed by the subject includes a resistance trainingexercise activity involving one or more weights (wt), one or more sets(s), each set including one or more repetitions (R), and a totalactivity time (T_(e)), and wherein the plurality of exercise parametervalues include: a. a set parameter value (n) representing the number ofthe one or more sets (s) for the resistance training activity; b. aweight parameter value (wt_(s)) for the weight associated with a set swhere s=1 to n; c. a repetition parameter value (R_(s)) representing thenumber of one or more repetitions in a set s where s=1 to n; and d. atotal activity time parameter value (T_(e)) being the time elapsed fromthe first repetition of the first set (s=1) to the final repetition ofthe final set (s=n) of the exercise activity.
 72. A method according toclaim 71 wherein the execution profile information and the load profileinformation include corresponding sequences of values, wherein eachvalue is associated with a different exercise phase of the exerciseactivity for a muscle of the subject intended to perform work during theexercise activity, and wherein the values associated with differentexercise phases of the exercise activity include values associated with:e. an eccentric phase; f. an eccentric-pause phase; g. an concentricphase; and h. a concentric-pause phase.
 73. A method according to claim71 further including accessing an exercise activity database to retrievea parameter indicating a proportion of the subject's bodyweightcontributing to a work performed by the exercise activity, anddetermining each weight parameter value (wt_(s)) based at least in parton the parameter indicating the proportion of the subject's bodyweight,the subject's bodyweight contributing to the work performed by theexercise activity, and an exercise load.
 74. A method according to claim72 wherein the load profile (LP) information identifies the exercisephases intended to contribute to work during execution of the exerciseactivity, and wherein the load profile (LP) information is expressed asa sequence values, the sequence including a value (d₁) identifying aneccentric phase contribution, a value (d₂) indicating an eccentric-pausephase contribution, a value (d₃) indicating a concentric phasecontribution, and a value (d₄) indicating an concentric-pause phasecontribution.
 75. A method according to claim 74 wherein the loadprofile (LP) information is expressed as the sequence [d₁, d₂, d₃, d₄],and wherein each value in the sequence is a binary digit having a firstvalue for indicating that the respective exercise phase is intended tocontribute to work, and a second value for indicating that therespective exercise phase is not intended to contribute to work.
 76. Amethod according to claim 74 wherein each value in the sequence ofvalues includes a scalar value indicating a proportion of a loadutilised in working a muscle during the respective exercise phase.
 77. Amethod according to claim 72 wherein the execution profile (EP)information includes a value (t₁) indicating the duration of theeccentric phase during the execution of the exercise activity, a value(t₂) indicating the duration of the eccentric-pause phase during theexecution of the exercise activity, a value (t₃) indicating the durationof the concentric phase during the execution of the exercise activityand a value (t₄) indicating the duration of the concentric-pause phaseduring the execution of the exercise activity.
 78. A method according toclaim 77, wherein the execution profile information is sensed duringexecution of the exercise activity.
 79. A method according to claim 77wherein processing the plurality of exercise parameter values and theactivity information to determine one or more assessment parametervalues for assessing the executed exercise activity includes determininga value of total time under tension (TUT) for each repetition:TUT _(R) =t _(1,r) ·d _(1,r) +t _(2,r) ·d _(t,2) +t _(3,r) ·d _(3,r) +t_(4,r) ·d _(4,r).
 80. A method according to claim 79 wherein for eachset (s) a single value of time under tension (TUT) is determined as anaverage value of time under tension for the repetitions of a set as:${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}{t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$81. A method according to claim 71 wherein the one or more assessmentparameter values for assessing the executed exercise activity include atleast one of: a. a work volume parameter value for the executed exerciseactivity (W); b. a work intensity parameter value for the executedexercise activity (W_(i)); c. a stress intensity parameter value for theexecuted exercise activity (S_(i)); and d. a hypertrophy factorparameter value for the executed exercise activity (H_(f)).
 82. A methodaccording to claim 81 wherein the work volume parameter value (W) isdetermined as:W=Σ _(s=1) ^(n) W _(s)=Σ_(s=1) ^(n)(R _(s) ·wt _(s)) where: n is thenumber of sets in the exercise activity; R_(s) is the number ofrepetitions in set s, where s=1 to n; and wt_(s) is the weightassociated with each set where s=1 to n.
 83. A method according to claim82 wherein the work intensity parameter value (W_(i)) is determined as:$W_{i} = \frac{W}{T_{e}}$
 84. A method according to claim 81 wherein foreach set (s) a single value of time under tension (TUT) is determined asan average value of time under tension for the repetitions of a set as:${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}{t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$85. A method according claim 84 wherein the stress intensity parametervalue (S_(i)) is determined as:$S_{i} = \frac{\sum\limits_{s = 1}^{n}{R_{s} \cdot {wt}_{s} \cdot {TUT}_{R}}}{T_{e}}$86. A method according to claim 85 wherein the hypertrophy factorparameter value (H_(f)) is determined as:H _(f) =S _(i) ·W
 87. A method of analysing a resistance exerciseactivity executed by a subject, the method including: receiving aplurality of exercise parameter values for the executed exerciseactivity into a processing device, the plurality of exercise parametervalues including information representing an execution profile (EP) forthe executed exercise activity; accessing a store of information toretrieve information representing a load profile (LP) for the executedexercise activity; and processing the plurality of exercise parametervalues and the information representing the load profile to determineone or more assessment parameter values for assessing the executedexercise activity; wherein the execution profile information and theload profile information include corresponding sequences of valuesassociated with a different exercise phase of the exercise activity fora muscle of the subject intended to perform work during the exerciseactivity, such that the values associated with different exercise phasesof the exercise activity include values associated with: an eccentricphase; an eccentric-pause phase; an concentric phase; and aconcentric-pause phase; and wherein the load profile (LP) informationidentifies the exercise phases intended to contribute to work duringexecution of the exercise activity, and the execution profile (EP)information identifies the duration of each exercise phase whichcontributed to work during execution of the exercise activity.
 88. Acomputer readable media including computer program instructions whichare executable by a processor to implement a method according to claim70.
 89. A system for analysing a resistance exercise activity executedby a subject, the system including: a processing unit programmed with aset of programmed instructions in the form of a computer softwareprogram; a store of information representing a load profile (LP) for theexecuted activity; and receiving means for receiving a plurality ofexercise parameter values for the executed exercise activity into theprocessing unit, the plurality of exercise parameter values includinginformation representing an execution profile (EP) for the executedexercise activity; wherein the processing unit retrieves the informationrepresenting a load profile (LP) for the executed exercise activity andprocesses the plurality of exercise parameter values of the executionprofile information and the information representing the load profile todetermine one or more assessment parameter values for assessing theexecuted exercise activity.
 90. A system according to claim 89 where theexercise activity executed by the subject includes a resistance trainingexercise activity involving one or more weights (wt), one or more ofsets (s), each set including one or more repetitions (R), and a totalactivity time (T_(e)), and wherein the plurality of exercise parametervalues includes: a. a set parameter value (n) representing the number ofthe one or more sets (s) for the resistance training activity; b. aweight parameter value (wt_(s)) for the weight associated with a set swhere s=1 to n; c. a repetition parameter value (R_(s)) representing thenumber of one or more repetitions in a set s where s=1 to n; and d. atotal activity time parameter value (T_(e)).
 91. A system according toclaim 89 wherein the execution profile information and the load profileinformation include corresponding sequences of values, wherein eachvalue is associated with a different exercise phase of the exerciseactivity, and wherein the values associated with different exercisephases of the exercise activity include values associated with: a. aneccentric phase; b. an eccentric-pause phase; c. a concentric phase; andd. a concentric-pause phase.
 92. A system according to claim 89 furtherincluding an exercise activity database which is accessible by theprocessing unit to: retrieve a parameter indicating a proportion of thesubject's bodyweight contributing to a work performed by the exerciseactivity; and determine each weight parameter value (wt_(s)) based atleast in part on the parameter indicating the proportion of thesubject's bodyweight, the subject's bodyweight contributing to the workperformed by the exercise activity, and an exercise load.
 93. A systemaccording to claim 91 wherein the load profile (LP) informationidentifies the exercise phases intended to contribute to work duringexecution of the exercise activity and wherein the load profile (LP)information is expressed as a sequence of values, the sequence includinga value (d₁) identifying an eccentric phase contribution, a value (d₂)indicating an eccentric-pause phase contribution, a value (d₃)indicating a concentric phase contribution, and a value (d₄) indicatingan concentric-pause phase contribution.
 94. A system according to claim92 wherein the load profile (LP) information is expressed as thesequence [d₁, d₂, d₃, d₄], and wherein each value in the sequence is abinary digit having a first value indicating that the respectiveexercise phase is intended to contribute to work, and a second valueindicating that the respective exercise phase is not intended tocontribute to work.
 95. A system according to claim 91, wherein theexecution profile (EP) information includes a value (t₁) indicating theduration of the eccentric phase during the execution of the exercise, avalue (t₂) indicating the duration of the eccentric-pause phase duringthe execution of the exercise, a value (t₃) indicating the duration ofthe concentric phase during the execution of the exercise and a value(t₄) indicating the duration of the concentric-pause phase during theexecution of the exercise.
 96. A system according to claim 95 whereinprocessing the plurality of exercise parameters values and the activityinformation to determine one or more assessment parameter values forassessing the executed exercise activity includes determining a value oftotal time under tension (TUT) for each repetition:TUT _(R) =t _(1,r) ·d _(1,r) +t _(2,r) ·d _(2,r) +t _(3,r) ·d _(3,r) +t_(4,r) ·d _(4,r)
 97. A system, according to claim 96 wherein for eachset (s) the single time under tension value (TUT) is determined as anaverage time under tension value for the repetitions of a set as:${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}{t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$98. A system according to claim 89 wherein the one or more assessmentparameter values for assessing the executed exercise activity include:a. a work volume parameter value for the executed exercise activity (W);b. a work intensity parameter value for the executed exercise activity(W_(i)); c. a stress intensity parameter value for the executed exerciseactivity (S_(i)); and d. a hypertrophy factor parameter value for theexecuted exercise activity (H_(f)).
 99. A system according to claim 98wherein the work volume parameter value (W) is determined as:$W = {{\sum\limits_{s = 1}^{n}W_{s}} = {\sum\limits_{s = 1}^{n}( {R_{s} \cdot {wt}_{s}} )}}$where: n is the number of sets in the exercise activity; R_(s) is thenumber of reps in set s, where s=1 to n; and wt_(s) is the weightassociated with set s where s=1 to n.
 100. A system according to claim99 wherein the work intensity parameter value (W) is determined as:$W_{i} = \frac{W}{T_{e}}$
 101. A method according to claim 100 whereinfor each set (s) a single value of time under tension (TUT) isdetermined as an average value of time under tension for the repetitionsof a set as:${TUT}_{R} = \frac{{\sum\limits_{r = 1}^{R_{s}}{t_{1,r} \cdot d_{1,r}}} + {t_{2,r} \cdot d_{2,r}} + {t_{3,r} \cdot d_{3,r}} + {t_{4,r} \cdot d_{4,r}}}{R_{s}}$102. A system according to claim 101, wherein the stress intensityparameter value (S_(i)) is determined as:$S_{i} = \frac{\sum\limits_{s = 1}^{n}{R_{s} \cdot {wt}_{s} \cdot {TUT}_{R}}}{T_{e}}$103. A system according to claim 98 wherein the hypertrophy factorparameter value (H_(f)) is determined as:H _(f) =S _(i) ·W